Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations41176
Missing cells39661
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.4 MiB
Average record size in memory698.4 B

Variable types

Numeric10
Categorical11
Boolean1

Alerts

cons.conf.idx is highly overall correlated with monthHigh correlation
cons.price.idx is highly overall correlated with contact and 2 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 2 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
month is highly overall correlated with cons.conf.idx and 5 other fieldsHigh correlation
nr.employed is highly overall correlated with contact and 3 other fieldsHigh correlation
pdays is highly overall correlated with pdays_not_contactedHigh correlation
pdays_not_contacted is highly overall correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly overall correlated with pdays_not_contacted and 1 other fieldsHigh correlation
previous is highly overall correlated with pdays_not_contacted and 1 other fieldsHigh correlation
default is highly imbalanced (53.3%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
poutcome is highly imbalanced (56.8%)Imbalance
pdays_not_contacted is highly imbalanced (77.3%)Imbalance
pdays has 39661 (96.3%) missing valuesMissing
previous has 35551 (86.3%) zerosZeros

Reproduction

Analysis started2025-12-06 15:41:59.761900
Analysis finished2025-12-06 15:42:22.794524
Duration23.03 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.0238
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:22.986498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42068
Coefficient of variation (CV)0.26036208
Kurtosis0.79111332
Mean40.0238
Median Absolute Deviation (MAD)7
Skewness0.78456026
Sum1648020
Variance108.59057
MonotonicityNot monotonic
2025-12-06T21:12:23.209833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311947
 
4.7%
321845
 
4.5%
331833
 
4.5%
361779
 
4.3%
351758
 
4.3%
341745
 
4.2%
301714
 
4.2%
371475
 
3.6%
291453
 
3.5%
391430
 
3.5%
Other values (68)24197
58.8%
ValueCountFrequency (%)
175
 
< 0.1%
1828
 
0.1%
1942
 
0.1%
2065
 
0.2%
21102
 
0.2%
22137
 
0.3%
23226
 
0.5%
24462
1.1%
25598
1.5%
26698
1.7%
ValueCountFrequency (%)
982
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
892
 
< 0.1%
8822
0.1%
871
 
< 0.1%
868
 
< 0.1%
8515
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
admin.
10419 
blue-collar
9253 
technician
6739 
services
3967 
management
2924 
Other values (7)
7874 

Length

Max length13
Median length12
Mean length8.9554352
Min length6

Characters and Unicode

Total characters368749
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin.10419
25.3%
blue-collar9253
22.5%
technician6739
16.4%
services3967
 
9.6%
management2924
 
7.1%
retired1718
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%
Other values (2)1205
 
2.9%

Length

2025-12-06T21:12:23.496058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin10419
25.3%
blue-collar9253
22.5%
technician6739
16.4%
services3967
 
9.6%
management2924
 
7.1%
retired1718
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%
Other values (2)1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e47260
12.8%
n35536
 
9.6%
a33319
 
9.0%
l31615
 
8.6%
i30642
 
8.3%
c26698
 
7.2%
r21024
 
5.7%
m19762
 
5.4%
d16507
 
4.5%
t14587
 
4.0%
Other values (14)91799
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)368749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e47260
12.8%
n35536
 
9.6%
a33319
 
9.0%
l31615
 
8.6%
i30642
 
8.3%
c26698
 
7.2%
r21024
 
5.7%
m19762
 
5.4%
d16507
 
4.5%
t14587
 
4.0%
Other values (14)91799
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)368749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e47260
12.8%
n35536
 
9.6%
a33319
 
9.0%
l31615
 
8.6%
i30642
 
8.3%
c26698
 
7.2%
r21024
 
5.7%
m19762
 
5.4%
d16507
 
4.5%
t14587
 
4.0%
Other values (14)91799
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)368749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e47260
12.8%
n35536
 
9.6%
a33319
 
9.0%
l31615
 
8.6%
i30642
 
8.3%
c26698
 
7.2%
r21024
 
5.7%
m19762
 
5.4%
d16507
 
4.5%
t14587
 
4.0%
Other values (14)91799
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
married
24921 
single
11564 
divorced
4611 
unknown
 
80

Length

Max length8
Median length7
Mean length6.8311395
Min length6

Characters and Unicode

Total characters281279
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married24921
60.5%
single11564
28.1%
divorced4611
 
11.2%
unknown80
 
0.2%

Length

2025-12-06T21:12:23.718769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:23.867907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married24921
60.5%
single11564
28.1%
divorced4611
 
11.2%
unknown80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)281279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)281279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)281279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
university.degree
12164 
high.school
9512 
basic.9y
6045 
professional.course
5240 
basic.4y
4176 
Other values (3)
4039 

Length

Max length19
Median length17
Mean length12.710462
Min length7

Characters and Unicode

Total characters523366
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree12164
29.5%
high.school9512
23.1%
basic.9y6045
14.7%
professional.course5240
12.7%
basic.4y4176
 
10.1%
basic.6y2291
 
5.6%
unknown1730
 
4.2%
illiterate18
 
< 0.1%

Length

2025-12-06T21:12:24.035421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:24.401176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
university.degree12164
29.5%
high.school9512
23.1%
basic.9y6045
14.7%
professional.course5240
12.7%
basic.4y4176
 
10.1%
basic.6y2291
 
5.6%
unknown1730
 
4.2%
illiterate18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e59172
 
11.3%
i51628
 
9.9%
s49908
 
9.5%
.39428
 
7.5%
o36474
 
7.0%
r34826
 
6.7%
h28536
 
5.5%
c27264
 
5.2%
y24676
 
4.7%
n22594
 
4.3%
Other values (15)148860
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)523366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e59172
 
11.3%
i51628
 
9.9%
s49908
 
9.5%
.39428
 
7.5%
o36474
 
7.0%
r34826
 
6.7%
h28536
 
5.5%
c27264
 
5.2%
y24676
 
4.7%
n22594
 
4.3%
Other values (15)148860
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)523366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e59172
 
11.3%
i51628
 
9.9%
s49908
 
9.5%
.39428
 
7.5%
o36474
 
7.0%
r34826
 
6.7%
h28536
 
5.5%
c27264
 
5.2%
y24676
 
4.7%
n22594
 
4.3%
Other values (15)148860
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)523366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e59172
 
11.3%
i51628
 
9.9%
s49908
 
9.5%
.39428
 
7.5%
o36474
 
7.0%
r34826
 
6.7%
h28536
 
5.5%
c27264
 
5.2%
y24676
 
4.7%
n22594
 
4.3%
Other values (15)148860
28.4%

default
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
no
32577 
unknown
8596 
yes
 
3

Length

Max length7
Median length2
Mean length3.0438848
Min length2

Characters and Unicode

Total characters125335
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no32577
79.1%
unknown8596
 
20.9%
yes3
 
< 0.1%

Length

2025-12-06T21:12:24.618538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:24.726823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no32577
79.1%
unknown8596
 
20.9%
yes3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)125335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)125335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)125335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

housing
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
yes
21571 
no
18615 
unknown
 
990

Length

Max length7
Median length3
Mean length2.6440888
Min length2

Characters and Unicode

Total characters108873
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes21571
52.4%
no18615
45.2%
unknown990
 
2.4%

Length

2025-12-06T21:12:24.862172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:24.965233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes21571
52.4%
no18615
45.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)108873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)108873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)108873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

loan
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
no
33938 
yes
6248 
unknown
 
990

Length

Max length7
Median length2
Mean length2.2719545
Min length2

Characters and Unicode

Total characters93550
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no33938
82.4%
yes6248
 
15.2%
unknown990
 
2.4%

Length

2025-12-06T21:12:25.099229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:25.199231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no33938
82.4%
yes6248
 
15.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)93550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)93550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)93550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

contact
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
cellular
26135 
telephone
15041 

Length

Max length9
Median length8
Mean length8.3652856
Min length8

Characters and Unicode

Total characters344449
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular26135
63.5%
telephone15041
36.5%

Length

2025-12-06T21:12:25.332647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:25.432636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cellular26135
63.5%
telephone15041
36.5%

Most occurring characters

ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)344449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)344449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)344449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

month
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
may
13767 
jul
7169 
aug
6176 
jun
5318 
nov
4100 
Other values (5)
4646 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123528
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may13767
33.4%
jul7169
17.4%
aug6176
15.0%
jun5318
 
12.9%
nov4100
 
10.0%
apr2631
 
6.4%
oct717
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Length

2025-12-06T21:12:25.554638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:25.733905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
may13767
33.4%
jul7169
17.4%
aug6176
15.0%
jun5318
 
12.9%
nov4100
 
10.0%
apr2631
 
6.4%
oct717
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)123528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)123528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)123528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
thu
8618 
mon
8512 
wed
8134 
tue
8086 
fri
7826 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123528
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu8618
20.9%
mon8512
20.7%
wed8134
19.8%
tue8086
19.6%
fri7826
19.0%

Length

2025-12-06T21:12:25.953141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:26.075491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
thu8618
20.9%
mon8512
20.7%
wed8134
19.8%
tue8086
19.6%
fri7826
19.0%

Most occurring characters

ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)123528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)123528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)123528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

duration
Real number (ℝ)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.31582
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:26.263634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile753
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.30532
Coefficient of variation (CV)1.0038306
Kurtosis20.243771
Mean258.31582
Median Absolute Deviation (MAD)94
Skewness3.2628075
Sum10636412
Variance67239.249
MonotonicityNot monotonic
2025-12-06T21:12:26.459639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85170
 
0.4%
90170
 
0.4%
136168
 
0.4%
73167
 
0.4%
124163
 
0.4%
87162
 
0.4%
104161
 
0.4%
72161
 
0.4%
111160
 
0.4%
106159
 
0.4%
Other values (1534)39535
96.0%
ValueCountFrequency (%)
04
 
< 0.1%
13
 
< 0.1%
21
 
< 0.1%
33
 
< 0.1%
412
 
< 0.1%
530
 
0.1%
637
0.1%
754
0.1%
869
0.2%
977
0.2%
ValueCountFrequency (%)
49181
< 0.1%
41991
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%
35091
< 0.1%
34221
< 0.1%
33661
< 0.1%
33221
< 0.1%
32841
< 0.1%

campaign
Real number (ℝ)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5678793
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:26.639631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7703183
Coefficient of variation (CV)1.0788351
Kurtosis36.971857
Mean2.5678793
Median Absolute Deviation (MAD)1
Skewness4.7620441
Sum105735
Variance7.6746637
MonotonicityNot monotonic
2025-12-06T21:12:26.825069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
117634
42.8%
210568
25.7%
35340
 
13.0%
42650
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
Other values (32)869
 
2.1%
ValueCountFrequency (%)
117634
42.8%
210568
25.7%
35340
 
13.0%
42650
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
ValueCountFrequency (%)
561
 
< 0.1%
432
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
355
< 0.1%
343
< 0.1%
334
< 0.1%

pdays
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)1.7%
Missing39661
Missing (%)96.3%
Infinite0
Infinite (%)0.0%
Mean6.0145215
Minimum0
Maximum27
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:26.993464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median6
Q37
95-th percentile14
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8249063
Coefficient of variation (CV)0.63594523
Kurtosis2.5645621
Mean6.0145215
Median Absolute Deviation (MAD)3
Skewness1.4585638
Sum9112
Variance14.629908
MonotonicityNot monotonic
2025-12-06T21:12:27.153461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3439
 
1.1%
6412
 
1.0%
4118
 
0.3%
964
 
0.2%
261
 
0.1%
760
 
0.1%
1258
 
0.1%
1052
 
0.1%
546
 
0.1%
1336
 
0.1%
Other values (16)169
 
0.4%
(Missing)39661
96.3%
ValueCountFrequency (%)
015
 
< 0.1%
126
 
0.1%
261
 
0.1%
3439
1.1%
4118
 
0.3%
546
 
0.1%
6412
1.0%
760
 
0.1%
818
 
< 0.1%
964
 
0.2%
ValueCountFrequency (%)
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
193
 
< 0.1%
187
< 0.1%
178
< 0.1%
1611
< 0.1%

previous
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17301341
Minimum0
Maximum7
Zeros35551
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:27.281117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49496438
Coefficient of variation (CV)2.8608441
Kurtosis20.102164
Mean0.17301341
Median Absolute Deviation (MAD)0
Skewness3.8313955
Sum7124
Variance0.24498974
MonotonicityNot monotonic
2025-12-06T21:12:27.413120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035551
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
035551
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
518
 
< 0.1%
470
 
0.2%
3216
 
0.5%
2754
 
1.8%
14561
 
11.1%
035551
86.3%

poutcome
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
nonexistent
35551 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.453565
Min length7

Characters and Unicode

Total characters430436
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent35551
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Length

2025-12-06T21:12:27.590114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:27.749732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent35551
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
x35551
 
8.3%
o35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)430436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
x35551
 
8.3%
o35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)430436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
x35551
 
8.3%
o35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)430436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
x35551
 
8.3%
o35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

emp.var.rate
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081921508
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17186
Negative (%)41.7%
Memory size643.4 KiB
2025-12-06T21:12:27.858733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5708826
Coefficient of variation (CV)19.17546
Kurtosis-1.062698
Mean0.081921508
Median Absolute Deviation (MAD)0.3
Skewness-0.72406059
Sum3373.2
Variance2.4676722
MonotonicityNot monotonic
2025-12-06T21:12:27.987734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.416228
39.4%
-1.89182
22.3%
1.17762
18.9%
-0.13682
 
8.9%
-2.91662
 
4.0%
-3.41070
 
2.6%
-1.7773
 
1.9%
-1.1635
 
1.5%
-3172
 
0.4%
-0.210
 
< 0.1%
ValueCountFrequency (%)
-3.41070
 
2.6%
-3172
 
0.4%
-2.91662
 
4.0%
-1.89182
22.3%
-1.7773
 
1.9%
-1.1635
 
1.5%
-0.210
 
< 0.1%
-0.13682
 
8.9%
1.17762
18.9%
1.416228
39.4%
ValueCountFrequency (%)
1.416228
39.4%
1.17762
18.9%
-0.13682
 
8.9%
-0.210
 
< 0.1%
-1.1635
 
1.5%
-1.7773
 
1.9%
-1.89182
22.3%
-2.91662
 
4.0%
-3172
 
0.4%
-3.41070
 
2.6%

cons.price.idx
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57572
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:28.131740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57883899
Coefficient of variation (CV)0.0061857818
Kurtosis-0.82985107
Mean93.57572
Median Absolute Deviation (MAD)0.38
Skewness-0.23085291
Sum3853073.8
Variance0.33505457
MonotonicityNot monotonic
2025-12-06T21:12:28.279795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9947762
18.9%
93.9186681
16.2%
92.8935793
14.1%
93.4445173
12.6%
94.4654374
10.6%
93.23615
8.8%
93.0752457
 
6.0%
92.201770
 
1.9%
92.963715
 
1.7%
92.431446
 
1.1%
Other values (16)3390
8.2%
ValueCountFrequency (%)
92.201770
 
1.9%
92.379267
 
0.6%
92.431446
 
1.1%
92.469177
 
0.4%
92.649357
 
0.9%
92.713172
 
0.4%
92.75610
 
< 0.1%
92.843282
 
0.7%
92.8935793
14.1%
92.963715
 
1.7%
ValueCountFrequency (%)
94.767128
 
0.3%
94.601204
 
0.5%
94.4654374
10.6%
94.215311
 
0.8%
94.199303
 
0.7%
94.055229
 
0.6%
94.027233
 
0.6%
93.9947762
18.9%
93.9186681
16.2%
93.876212
 
0.5%

cons.conf.idx
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.502863
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41176
Negative (%)100.0%
Memory size643.4 KiB
2025-12-06T21:12:28.424495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.62786
Coefficient of variation (CV)-0.11426007
Kurtosis-0.35909705
Mean-40.502863
Median Absolute Deviation (MAD)4.4
Skewness0.302876
Sum-1667745.9
Variance21.417088
MonotonicityNot monotonic
2025-12-06T21:12:28.581494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.47762
18.9%
-42.76681
16.2%
-46.25793
14.1%
-36.15173
12.6%
-41.84374
10.6%
-423615
8.8%
-47.12457
 
6.0%
-31.4770
 
1.9%
-40.8715
 
1.7%
-26.9446
 
1.1%
Other values (16)3390
8.2%
ValueCountFrequency (%)
-50.8128
 
0.3%
-50282
 
0.7%
-49.5204
 
0.5%
-47.12457
 
6.0%
-46.25793
14.1%
-45.910
 
< 0.1%
-42.76681
16.2%
-423615
8.8%
-41.84374
10.6%
-40.8715
 
1.7%
ValueCountFrequency (%)
-26.9446
 
1.1%
-29.8267
 
0.6%
-30.1357
 
0.9%
-31.4770
 
1.9%
-33172
 
0.4%
-33.6177
 
0.4%
-34.6174
 
0.4%
-34.8264
 
0.6%
-36.15173
12.6%
-36.47762
18.9%

euribor3m
Real number (ℝ)

High correlation 

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6212934
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:28.760495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734437
Coefficient of variation (CV)0.47895511
Kurtosis-1.4067913
Mean3.6212934
Median Absolute Deviation (MAD)0.108
Skewness-0.70919421
Sum149110.38
Variance3.0082717
MonotonicityNot monotonic
2025-12-06T21:12:28.972586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572868
 
7.0%
4.9622611
 
6.3%
4.9632487
 
6.0%
4.9611902
 
4.6%
4.8561210
 
2.9%
4.9641175
 
2.9%
1.4051169
 
2.8%
4.9651070
 
2.6%
4.8641044
 
2.5%
4.961013
 
2.5%
Other values (306)24627
59.8%
ValueCountFrequency (%)
0.6348
 
< 0.1%
0.63543
0.1%
0.63614
 
< 0.1%
0.6376
 
< 0.1%
0.6387
 
< 0.1%
0.63916
 
< 0.1%
0.6410
 
< 0.1%
0.64235
0.1%
0.64323
0.1%
0.64438
0.1%
ValueCountFrequency (%)
5.0459
 
< 0.1%
57
 
< 0.1%
4.97172
 
0.4%
4.968991
 
2.4%
4.967643
 
1.6%
4.966620
 
1.5%
4.9651070
2.6%
4.9641175
2.9%
4.9632487
6.0%
4.9622611
6.3%

nr.employed
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.0349
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size643.4 KiB
2025-12-06T21:12:29.129593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.251364
Coefficient of variation (CV)0.013983138
Kurtosis-0.0035396701
Mean5167.0349
Median Absolute Deviation (MAD)37.1
Skewness-1.0443171
Sum2.1275783 × 108
Variance5220.2596
MonotonicityNot monotonic
2025-12-06T21:12:29.261582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.116228
39.4%
5099.18532
20.7%
51917762
18.9%
5195.83682
 
8.9%
5076.21662
 
4.0%
5017.51070
 
2.6%
4991.6773
 
1.9%
5008.7650
 
1.6%
4963.6635
 
1.5%
5023.5172
 
0.4%
ValueCountFrequency (%)
4963.6635
 
1.5%
4991.6773
 
1.9%
5008.7650
 
1.6%
5017.51070
 
2.6%
5023.5172
 
0.4%
5076.21662
 
4.0%
5099.18532
20.7%
5176.310
 
< 0.1%
51917762
18.9%
5195.83682
8.9%
ValueCountFrequency (%)
5228.116228
39.4%
5195.83682
 
8.9%
51917762
18.9%
5176.310
 
< 0.1%
5099.18532
20.7%
5076.21662
 
4.0%
5023.5172
 
0.4%
5017.51070
 
2.6%
5008.7650
 
1.6%
4991.6773
 
1.9%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.9 KiB
False
36537 
True
4639 
ValueCountFrequency (%)
False36537
88.7%
True4639
 
11.3%
2025-12-06T21:12:29.361593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

pdays_not_contacted
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
39661 
1
 
1515

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039661
96.3%
11515
 
3.7%

Length

2025-12-06T21:12:29.480588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T21:12:29.572586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
039661
96.3%
11515
 
3.7%

Most occurring characters

ValueCountFrequency (%)
039661
96.3%
11515
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)41176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
039661
96.3%
11515
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
039661
96.3%
11515
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
039661
96.3%
11515
 
3.7%

Interactions

2025-12-06T21:12:20.387645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:05.883200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:07.793918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:09.341205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:10.852920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:12.524879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:14.097492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:15.610486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:17.073093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:18.746815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:20.546143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:06.081315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:07.967478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:09.500848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:11.009909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:12.683876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:14.261271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:15.767495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:17.230366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:18.908877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:20.707512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:06.271714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:08.107733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:09.659959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:11.157896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:12.845450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:14.432097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:15.912477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:17.573722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:19.144168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:20.855234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:06.428179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:08.219883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:09.796638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:11.302004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:12.992459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:14.559195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:16.068160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:17.717713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:19.289557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:20.999272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:06.590246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:08.375746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:09.937767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:11.452013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:13.140449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:14.698625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:16.211013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:17.873655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:19.444308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:21.168230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:06.753885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:08.528757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:10.108858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:11.590995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:13.309404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:14.849459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:16.360992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:18.033655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:19.624374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:21.296694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:06.942292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:08.676097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:10.267934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:11.742005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:13.485410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:14.986496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:16.501837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:18.173659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:19.764375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:21.493609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:07.166163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:08.824227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:10.408941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:11.881999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:13.650435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:15.141567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:16.653760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:18.290174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:19.906809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:21.649952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:07.323262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:08.977501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:10.559942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:12.197515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:13.797490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:15.280561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:16.787765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:18.445432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:20.054792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:21.789409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:07.479693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:09.170131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:10.702905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:12.373874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:13.942496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:15.465473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:16.931094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:18.593853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T21:12:20.260907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-06T21:12:29.752338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agecampaigncons.conf.idxcons.price.idxcontactday_of_weekdefaultdurationeducationemp.var.rateeuribor3mhousingjobloanmaritalmonthnr.employedpdayspdays_not_contactedpoutcomepreviousy
age1.0000.0060.1140.0450.0990.0250.146-0.0020.1170.0450.0540.0000.2490.0100.2620.0940.045-0.0560.1390.109-0.0130.172
campaign0.0061.000-0.0010.0960.0640.0180.017-0.0810.0020.1560.1410.0220.0000.0210.0000.0470.1440.0590.0370.047-0.0870.052
cons.conf.idx0.114-0.0011.0000.2460.4170.0450.138-0.0090.0640.2250.2370.0400.1090.0110.0720.6000.133-0.0150.3880.369-0.1160.386
cons.price.idx0.0450.0960.2461.0000.6750.0500.1540.0030.0980.6650.4910.0690.1310.0170.0690.6760.4650.1550.3990.386-0.2830.336
contact0.0990.0640.4170.6751.0000.0550.1360.0320.1230.4620.4690.0850.1280.0240.0720.6090.5020.0100.1180.2420.2420.145
day_of_week0.0250.0180.0450.0500.0551.0000.0110.0080.0200.0350.1370.0150.0160.0060.0110.0670.0460.0720.0120.0150.0000.023
default0.1460.0170.1380.1540.1360.0111.0000.0000.1700.1570.1590.0110.1520.0020.0950.1120.1400.0000.0800.0770.0750.099
duration-0.002-0.081-0.0090.0030.0320.0080.0001.0000.000-0.069-0.0780.0000.0060.0000.0000.020-0.0950.1020.0290.0170.0420.377
education0.1170.0020.0640.0980.1230.0200.1700.0001.0000.0660.0600.0130.3590.0000.1160.0950.0670.0490.0550.0420.0190.067
emp.var.rate0.0450.1560.2250.6650.4620.0350.157-0.0690.0661.0000.9400.0520.1350.0120.0680.6590.9450.1490.3520.380-0.4350.342
euribor3m0.0540.1410.2370.4910.4690.1370.159-0.0780.0600.9401.0000.0520.1280.0120.0680.5520.929-0.0910.4370.418-0.4550.399
housing0.0000.0220.0400.0690.0850.0150.0110.0000.0130.0520.0521.0000.0110.7080.0090.0540.0400.0240.0080.0170.0160.010
job0.2490.0000.1090.1310.1280.0160.1520.0060.3590.1350.1280.0111.0000.0100.1840.1100.1340.0420.1400.1000.0530.152
loan0.0100.0210.0110.0170.0240.0060.0020.0000.0000.0120.0120.7080.0101.0000.0000.0200.0100.0000.0000.0000.0000.000
marital0.2620.0000.0720.0690.0720.0110.0950.0000.1160.0680.0680.0090.1840.0001.0000.0500.0720.0000.0420.0370.0300.054
month0.0940.0470.6000.6760.6090.0670.1120.0200.0950.6590.5520.0540.1100.0200.0501.0000.6020.1770.2400.2420.1270.274
nr.employed0.0450.1440.1330.4650.5020.0460.140-0.0950.0670.9450.9290.0400.1340.0100.0720.6021.000-0.1650.4570.412-0.4390.410
pdays-0.0560.059-0.0150.1550.0100.0720.0000.1020.0490.149-0.0910.0240.0420.0000.0000.177-0.1651.0001.0000.371-0.0010.098
pdays_not_contacted0.1390.0370.3880.3990.1180.0120.0800.0290.0550.3520.4370.0080.1400.0000.0420.2400.4571.0001.0000.9520.5970.325
poutcome0.1090.0470.3690.3860.2420.0150.0770.0170.0420.3800.4180.0170.1000.0000.0370.2420.4120.3710.9521.0000.7340.320
previous-0.013-0.087-0.116-0.2830.2420.0000.0750.0420.019-0.435-0.4550.0160.0530.0000.0300.127-0.439-0.0010.5970.7341.0000.236
y0.1720.0520.3860.3360.1450.0230.0990.3770.0670.3420.3990.0100.1520.0000.0540.2740.4100.0980.3250.3200.2361.000

Missing values

2025-12-06T21:12:22.086129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-06T21:12:22.477296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedypdays_not_contacted
056housemaidmarriedbasic.4ynononotelephonemaymon2611NaN0nonexistent1.193.994-36.44.8575191.0no0
157servicesmarriedhigh.schoolunknownnonotelephonemaymon1491NaN0nonexistent1.193.994-36.44.8575191.0no0
237servicesmarriedhigh.schoolnoyesnotelephonemaymon2261NaN0nonexistent1.193.994-36.44.8575191.0no0
340admin.marriedbasic.6ynononotelephonemaymon1511NaN0nonexistent1.193.994-36.44.8575191.0no0
456servicesmarriedhigh.schoolnonoyestelephonemaymon3071NaN0nonexistent1.193.994-36.44.8575191.0no0
545servicesmarriedbasic.9yunknownnonotelephonemaymon1981NaN0nonexistent1.193.994-36.44.8575191.0no0
659admin.marriedprofessional.coursenononotelephonemaymon1391NaN0nonexistent1.193.994-36.44.8575191.0no0
741blue-collarmarriedunknownunknownnonotelephonemaymon2171NaN0nonexistent1.193.994-36.44.8575191.0no0
824techniciansingleprofessional.coursenoyesnotelephonemaymon3801NaN0nonexistent1.193.994-36.44.8575191.0no0
925servicessinglehigh.schoolnoyesnotelephonemaymon501NaN0nonexistent1.193.994-36.44.8575191.0no0
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedypdays_not_contacted
4117862retiredmarrieduniversity.degreenononocellularnovthu48326.03success-1.194.767-50.81.0314963.6yes1
4117964retireddivorcedprofessional.coursenoyesnocellularnovfri1513NaN0nonexistent-1.194.767-50.81.0284963.6no0
4118036admin.marrieduniversity.degreenononocellularnovfri2542NaN0nonexistent-1.194.767-50.81.0284963.6no0
4118137admin.marrieduniversity.degreenoyesnocellularnovfri2811NaN0nonexistent-1.194.767-50.81.0284963.6yes0
4118229unemployedsinglebasic.4ynoyesnocellularnovfri11219.01success-1.194.767-50.81.0284963.6no1
4118373retiredmarriedprofessional.coursenoyesnocellularnovfri3341NaN0nonexistent-1.194.767-50.81.0284963.6yes0
4118446blue-collarmarriedprofessional.coursenononocellularnovfri3831NaN0nonexistent-1.194.767-50.81.0284963.6no0
4118556retiredmarrieduniversity.degreenoyesnocellularnovfri1892NaN0nonexistent-1.194.767-50.81.0284963.6no0
4118644technicianmarriedprofessional.coursenononocellularnovfri4421NaN0nonexistent-1.194.767-50.81.0284963.6yes0
4118774retiredmarriedprofessional.coursenoyesnocellularnovfri2393NaN1failure-1.194.767-50.81.0284963.6no0